Package: binaryRL 0.9.11

binaryRL: Reinforcement Learning Tools for Two-Alternative Forced Choice Tasks

Tools for building Rescorla-Wagner Models for Two-Alternative Forced Choice tasks, commonly employed in psychological research. Most concepts and ideas within this R package are referenced from Sutton and Barto (2018) <ISBN:9780262039246>. The package allows for the intuitive definition of RL models using simple if-else statements and three basic models built into this R package are referenced from Niv et al. (2012) <doi:10.1523/JNEUROSCI.5498-10.2012>. Our approach to constructing and evaluating these computational models is informed by the guidelines proposed in Wilson & Collins (2019) <doi:10.7554/eLife.49547>. Example datasets included with the package are sourced from the work of Mason et al. (2024) <doi:10.3758/s13423-023-02415-x>.

Authors:YuKi [aut, cre]

binaryRL_0.9.11.tar.gz
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binaryRL_0.9.11.tgz(r-4.6-x86_64)binaryRL_0.9.11.tgz(r-4.6-arm64)binaryRL_0.9.11.tgz(r-4.5-x86_64)binaryRL_0.9.11.tgz(r-4.5-arm64)
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binaryRL_0.9.11.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
binaryRL/json (API)

# Install 'binaryRL' in R:
install.packages('binaryRL', repos = c('https://yuki-961004.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/yuki-961004/binaryrl/issues

Pkgdown/docs site:https://yuki-961004.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

mapmlepsychologyrcppreinforcement-learningtafccpp

5.00 score 9 stars 6 scripts 597 downloads 16 exports 14 dependencies

Last updated from:6078e06742. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK124
linux-devel-x86_64OK129
source / vignettesOK227
linux-release-arm64OK133
linux-release-x86_64OK116
macos-release-arm64OK138
macos-release-x86_64OK142
macos-oldrel-arm64OK115
macos-oldrel-x86_64OK210
windows-develOK98
windows-releaseOK119
windows-oldrelOK123
wasm-releaseOK115

Exports:fit_pfunc_epsilonfunc_etafunc_gammafunc_loglfunc_pifunc_tauoptimize_pararcv_drecovery_datarpl_eRSTDrun_msimulate_listTDUtility

Dependencies:codetoolsdigestdoFuturedoRNGforeachfuturefuture.applyglobalsiteratorslistenvparallellyprogressrRcpprngtools